A Method for Batch Allocation of Equipment Maintenance Tasks Considering Dynamic Importance

Aiming at the problem that existing equipment importance evaluation methods fail to consider interconnectivity between pieces of equipment, variability after maintenance, and the impact of dynamically changing situations on importance, and focusing on the dynamic support needs of equipment in a conflict environment, this paper proposes a batch allocation method for equipment maintenance tasks considering dynamic importance. The purpose of this study is to determine the batch priority of equipment maintenance based on the dynamically changing importance of pieces of equipment. First, a dynamic importance index system is constructed: a real-time CRITIC-AHP combined weighting method is used to calculate team importance, a dynamic Bayesian network (DBN)-influenced method is used to calculate relative importance, an attention–LSTM time-series prediction method is used to calculate future importance, and then a dynamic entropy weight method is adopted to objectively integrate the three types of importance. Second, a dual-objective optimization model with the maximum equipment importance and the minimum total maintenance time is built, with mobile distance, maintenance time, and maintenance capacity as constraints. The Dynamic Particle Swarm Optimization (DPSO) algorithm is used to solve this model, and its dynamic adaptability is improved through environmental change detection and adaptive adjustment of inertia weight. Finally, the batch allocation of maintenance tasks is realized. Example verification shows that compared with the expert scoring method, the errors of the three importance calculation methods are all reduced by more than 60%, the optimization speed of the dynamic PSO algorithm is 47% faster than that of the static algorithm, and the constructed model has good stability. This method can provide a reference for maintenance support command decisions.